Human fungal infections represent a rapidly emerging global health threat, especially threatening immunocompromised populations, highlighting the urgent need for accurate and timely diagnostic approaches to reduce morbidity and mortality. This review synthesizes recent advances in diagnostic methodologies, including serological assays, point-of-care diagnostics, polymerase chain reaction (PCR)-based and sequencing technologies, as well as artificial intelligence (AI)- and machine learning (ML)-powered tools. Emerging diagnostic approaches have demonstrated notable improvements in detection accuracy, turnaround time, and antifungal resistance profiling capabilities, especially for drug-resistant strains. Nevertheless, substantial challenges persist in terms of standardization, scalability, cost-effectiveness, and implementation, particularly in resource-constrained settings. Future efforts should be directed toward the continuous innovation of rapid, sensitive, and multiplex diagnostic platforms for the simultaneous detection of fungi, bacteria, and viruses. Such advances may accelerate result acquisition, enhance diagnostic accuracy, support the development of more targeted therapeutic strategies, and ultimately improve clinical outcomes for patients.

The “superbug” Candida auris has been ranked as a priority fungal pathogen and is becoming a serious threat to public health. However, the underlying mechanisms of real-world pathogen–host interactions remain elusive, in part due to the lack of powerful immunocompetent animal models. Here, we report that selected wild-type strains of Drosophila melanogaster can be developed as a promising infection model to recapitulate C. auris systemic infection. The systemic and organ-specific responses to C. auris infection in vivo were evaluated, as well as the corresponding transcriptional profiling. Our findings confirmed that Toll and JAK-STAT signaling pathways mediate antifungal responses in the Drosophila model following C. auris infection. Moreover, we identified certain conserved novel factors required for host–C. auris interactions, highlighting the fly model's potential to reveal subtle immune mechanisms not readily observed in mammalian systems. Taken together, our work demonstrates that wild-type Drosophila offers a robust immunocompetent animal model for further in-depth investigation of dynamic C. auris–host interactions in vivo.

The alteration of microbiota in different host niches has been associated with numerous adverse health outcomes that affect millions of human patients annually. In last few years the interest has been increased to study the microbiota that is present in the female genital tract and may associate with various disease symptoms. Vaginal microbiota contributes to the maintenance of a healthy vaginal environment and simultaneously reduces the deleterious effect of various infectious agents. The change of vaginal microbiota has been observed using culture-independent methods such as next generation sequencing (NGS) technology, and its fluctuation was found to link with various endogenous and exogenous factors. Among them, a dynamic interplay between the vaginal microbiota and human papillomavirus (HPV) was found to be important in the acquisition and persistence of HPV and contributed to the development of cervical intraepithelial neoplasia (CIN) and cervical cancer (CC). Accumulating evidence has shown that persistent HPV infection depends upon regulation of the immune response and its associated factors such as hormones, human genetics and vaginal microbial interactions. Here, we reviewed the association of the microbiota with HPV persistence, factors in shaping the vaginal microbiota and their interplay with the regulation of immuno-regulatory molecules. The understanding of these associations could help identify important targets having impact on HPV persistence. Further, we discussed the predictive diagnostic and clinical therapeutic applications of vaginal microbiota in prevention and treatment of HPV persistence and subsequent progression of CIN and CC.